102 citations · 354 across the 7 of their papers we have counts for
7 papers
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
Fanfan Lin, Junhua Liu, Xinze Li +4
This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design. By harnessing in-context learning and specialized tiered…
Data-Driven Modeling with Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter
Xinze Li, Josep Pou, Jiaxin Dong +4
For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent…
Feature-aware conditional GAN for category text generation
Xinze Li, Kezhi Mao, Fanfan Lin +1
Category text generation receives considerable attentions since it is beneficial for various natural language processing tasks. Recently, the generative adversarial network (GAN) h…
Particle swarm optimization with state-based adaptive velocity limit strategy
Xinze Li, Kezhi Mao, Fanfan Lin +1
Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive V…
Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter with Minimized Current Stress
Xinze Li, Xin Zhang, Fanfan Lin +2
The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase sh…
Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter with Full ZVS Range and Optimal Efficiency
Xinze Li, Xin Zhang, Fanfan Lin +2
Dual active bridge (DAB) converter is the key enabler in many popular applications such as wireless charging, electric vehicle and renewable energy. ZVS range and efficiency are tw…